Documents about Error Bars

  • 3 Pages

    lec20

    Dallas, PSY 3393

    Excerpt: ... Dates PSY 3393 Experimental Projects Spring 2008 Dr. Peter Assmann Draft Introduction section due date: Thu Mar 27 (email is fine) Draft Methods section due date: Tue Apr 8 Hypothetical Data Writeup Before you collect the data for your second project, use the predictions you made in the Introduction section to develop (i.e., make up) a set of hypothetical data that fit these predictions. predictions Note: the data you report in your actual project must be collected, not made up! Write up these hypothetical results in APA format. Use "dummy" (hypothetical) values for F, df, and p. Date due: April 15 Introduction section The Introduction section places the study in a broader context and shows the reader where it fits into the literature. What do we already know about the topic? What missing pieces does the study fill in? Why is it important? Error Bars Error bars reflect the variability in scores around the means in two or more conditions. D Download the Excel l d th E l sprea ...

  • 9 Pages

    mc

    NYU, SCICOMP 2003

    Excerpt: ... less significant. Statistical error estimates are represented through error bars , which are related to statistical confidence intervals. A confidence interval is a computed interval [A- , A+ ] with the property that E[A] [A- , A+ ] with probability pc (the confidence probability). Typically statisticians use pc = 99% or pc = 99%. Of course, E[A] is not random, but A- and A+ are computed during the course of the Monte Carlo computation and are random. For pc = 95%, there is a 5% chance that A- > E[A] or A+ < E[A]. If I repeat the Monte Carlo computation a hundred times (with a different seed), E[A] remains the same unknown value and in all but about five of the runs, it is inside the computed confidence interval. The error bars used by Monte Carlo practitioners tend to be pc 33% confidence intervals because they prefer to give a realistic idea how large the statistical error is likely to be than to be right a vast majority of the time. 2 This quite simple in all but the most sophisticated Monte Carlo. We ...

  • 4 Pages

    discussion-sol

    Maryland, ASTR 220

    Excerpt: ... as some of the major mass extinctions. The question is whether or not this is coincidence. There is some evidence against this idea: we have many geologic processes on-going today, such as volcanism and sea floor spreading, and yet we have not had a large impact (to our knowledge) since the hypothetical Eocene-Oligocene mass extinction about 35 million years ago (Ch. 11, Table 4). Is the current geologic activity on the Earth still powered by that impact? Or is there another cause? Part 2, dealing with the article on the research by Gerta Keller. 1. From NCC, list at least four different age determinations of the Chicxulub crater, including their error bars . ( Error bars are the plus/minus numbers after the ages. They indicate the range of ages that the result includes. For example, if I measured your age as 21 1yr, I would consider my measurement to be accurate if your age was 20, 21, or 22 yrs.) Give the page numbers where you got the ages. Do you think that it would be easy for Dr. Keller to distinguish l ...

  • 6 Pages

    exp4

    Michigan State University, PHY 191

    Excerpt: ... ently close? 4) Next, extend your spreadsheet to calculate the deviations (= residuals), and their squares, from the linear fit, and use them to evaluate Eqs 8.15 - 8.17. Eq 8.15 is what you use to estimate y (the uncertainty in y measurements). This equation assumes y to be the same for each individual measurement. Its the method to use when you dont have any other way to estimate y. Eq 8.16-8.17 relate an estimate of the y (whether from 8.15, or from another method) to find the uncertainty in the intercept and slope. Kgraph uses methods like Eqs 8.15-17 to estimate parameter errors unless the weighted fit box is checked, in which case the data error bars are used. 5) Now calculate t to test to see whether your fit slope is consistent with your handmeasured slope. 1. Goals 1.1 To quantitatively study the time dependence of the velocity and position of a body falling freely under the influence of gravity. 1.2 To use least squares fitting methods to obtain best values and uncertainties for ...

  • 6 Pages

    exp4

    Michigan State University, PHY 191

    Excerpt: ... ntly close? 4) Next, extend your spreadsheet to calculate the deviations (= residuals), and their squares, from the linear fit, and use them to evaluate Eqs 8.15 - 8.17. Eq 8.15 is what you use to estimate y (the uncertainty in y measurements). This equation assumes y to be the same for each individual measurement. Its the method to use when you dont have any other way to estimate y. Eq 8.16-8.17 relate an estimate of the y (whether from 8.15, or from another method) to find the uncertainty in the intercept and slope. Kgraph uses methods like Eqs 8.15-17 to estimate parameter errors unless the weighted fit box is checked, in which case the data error bars are used. 5) Now calculate t to test to see whether your fit slope is consistent with your handmeasured slope. 1. Goals 1.1 To quantitatively study the time dependence of the velocity and position of a body falling freely under the influence of gravity. 1.2 To use least squares fitting methods to obtain best values and uncertainties for ...

  • 23 Pages

    Lab_4_2009

    Swarthmore, PHYSICS 50

    Excerpt: ... formulas. Next we want to obtain an associated error bar, a j , for the curve fit parameter aj. Using the standard law of propagation of errors, we have 2 N a j 2 2 aj = i i =1 yi using the results above for a1 and a2 and doing some algebra we get a1 = Notice that a j is independent of yi . the parameters is S x 2 x 2 2 ( x ) , a2 = S S x 2 ( x ) 2 If the error bars on the data are constant ( i = 0 ), the error in a1 = 0 N x = x2 x 2 x 2 , a2 = 0 N 1 x 2 x 2 1 1 x , x2 = N x2 N Finally, if the data set does not have an associated set of error bars , we can estimate 0 from the data Note that this sample variance is normalized to N-2 since we have already extracted tow parameters a1 and a2 from the data. Many non-linear curve-fitting problems may be transformed into linear problems by a simple change of variable. For example, Page 4 1 N 2 (standard-deviat ...

  • 1 Pages

    10p

    UPenn, VHM 801

    Excerpt: ... Notes for Exercises in Session 10 12:5,27x,56,57,21; 6:86; 12:22,34,35; 15:24 (12:29x; 15:33,37; midterm 2001); note suggested order, review of analysis after the ANOVA table, individual work on the exercises, time for questions on home assignment III. Minitab for 1-way ANOVA:1 Stat-ANOVA-One Way (best menu to use; not Unstacked!); for Bonferroni corrections: use Comparisons menu and Fisher (LSD) method with manually corrected error level , to plot group means with error bars : Stat-ANOVA-Interval Plot (with groups), right-click on interval bar, Edit Interval Bar-Options-Pool to display condence intervals based on pooled standard dev. Notes and questions for specic exercises: 12.27x: from lecture 10L; solution found under exercise 12.32 in lecture 10 of 2004, 15.24: analyze also by 1-way ANOVA procedure and pay attention to model assumptions, 12.29x: no data available (only summaries), so analysis must be done by hand (calculator); forget about R2, midterm 200 ...

  • 2 Pages

    v18n2p91-92

    Indiana State, V 18

    Excerpt: ... ). Note that the Opticells apparently mimic a static flask condition rather than a shaking flask condition. These results suggest that our culture conditions support the growth of C. elegans and S. cerevisiae in a manner comparable to conventional culturing methods. Figure 2. Growth of C. elegans in OptiCells with various starting densities. Animals were inoculated to a density of 10, 100, or 1000 worms/ml 10 ml of CeMM in OptiCells and incubated at 20 C. Worms were periodically withdrawn and counted. Error bars show the standard deviation from the mean. N = 3. Figure 3. Growth of S. cerevisiae in Opticells. Stationary phase S. cerevisiae BY4743 were diluted 1:1000 in YPD broth, transferred to shaker flasks or Opticells, and incubated at 30 C. The flasks were incubated either with shaking or statically. The OptiCells were incubated statically with a 2mm space between replicate Opticells. Growth was monitored over time by measuring the optical density at 600 nm. Error bars depict the standard ...

  • 1 Pages

    Homework2

    NYU, MONTECARLO 07

    Excerpt: ... Monte Carlo Methods, Fall 2007, Courant Institute, NYU Homework 2, Due October 16 1. Verify the recurrence relation for vk (x) in Section 3.2 of the SDE lecture notes. Use this recurrence relation to verify by induction that Lvk = -kvk . 2. Read the Monte Carlo lecture notes chapter from Scientific Computing to see how to make error bars for simple samplers. 3. Let f (x) = C 1 - x2 be the marginal distribution for the pair (X, Y ) uniformly distributed within the unit disk X 2 + Y 2 1. Show that sampling X by rejection from the one dimensional uniform distribution gives the same algorithm as sampling the pair (X, Y ) within the unit disk by rejection as in homework 1. 4. Make a plot of a Monte Carlo estimate of the exponential moment function Z() = Ef eX for in the range 0 10. Use one set of samples of f for the whole curve. Plot the estimate and the error bar for a not very large L (sample size) value and a fairly large value (two plots, one for each L value, each having the estimate together with a o ...

  • 11 Pages

    lecture21

    UCSD, MAE 127

    Excerpt: ... ra with error bars . Now let's examine some examples. Figure 4 shows sample spectra for the La Jolla sea level record (with error bars ) for two cases. In one, a year's worth of data was separated into 12 month-long segments. In the other the data were divided into 3 4-month-long segments. The basic spectral shapes are similar, but the background noise level and the error bar are larger when fewer segments are used. We have one final detail to consider with these spectra. Here we've plotted the frequency on a linear axis, but often people plot both dimensions on logarithmic axes as shown in Figure 5. Among other things, this let's us look visually for a simple spectral slope k to represent the decay of energy with increasing frequency: s = exp(-k). Smoothing or Filtering People study tide gauge records not only to learn about tides, but also to assess long-term changes in sea level. How can we take the tides out of the La Jolla sea level record? One possibility would be to use a tide model to predict the tides ...

  • 5 Pages

    asgt08

    Michigan State University, FOR 430

    Excerpt: ... Variables" dialog box (see Figure 8.03). Click on the score variable name to highlight it and then click on the arrow button to move score to the " Error Bars :" box. SW 430 - Assignment 8 - 3 of 6 Figure 8.03: Define Simple Error Bar Dialog Box Now click on the "OK" button to run the analysis. SPSS will create an error bar chart in the output window. DO NOTHING WITH THE OUTPUT AT THIS TIME. Instead, click on the tab at the bottom of the screen labeled conf.sav SPSS Data (see Figure 8.04). This will take you back to the SPSS data editor. Figure 8.04: conf.sav SPSS Data Tab SW 430 - Assignment 8 - 4 of 6 Set Up Analysis Choose the "Analyze" menu header, the "Descriptive Statistics" submenu, and the "Descriptives. . ." option (see Figure 8.05). Figure 8.05: SPSS Data Editor Window Descriptives This takes you to the "Descriptives" dialog box (see Figure 8.06). Click on the score variable name to highlight it and then click on the arrow bu ...